Forensic watermarking is used to track down digital pirates after they illegally redistribute video content. Although existing algorithms often resist common signal processing attacks, they are not always robust against camcording attacks. As a solution in the state of the art, registration methods are used to align the attacked video to the original one. However, watermark detection still fails when the quality is sufficiently decreased or when exposed to targeted attacks. Therefore, this paper proposes a novel fallback system that aims to detect the watermark when traditional methods fail. More concretely, we demonstrate that a primary watermark embedded by a traditional scheme indirectly creates a secondary watermark signal during video encoding. This secondary watermark consists of compression artifacts and is detected by the fallback system. Additionally, the proposed system incorporates video registration to cope with camcording attacks. The experimental results indicate that the fallback system has a striking increase in robustness compared to the existing methods. For example, the observed false-negative rate for targeted attacks improves from 100% to 0%. Moreover, the fallback is camcording resistant even when the traditional method combined with registration is not. In conclusion, the proposed system can be used as a fallback when traditional detection fails.

Video watermarking is a well-established technology to help identify digital pirates when they illegally re-distribute multimedia content. In order to provide every client with a unique, watermarked video, the traditional distribution architectures separately encode each watermarked video. However, since these encodings require a high amount of computational resources, such architectures do not scale well to a large number of users. Therefore, this paper proposes a novel architecture that uses fast encoders instead of traditional, full encoders. The fast encoders re-use the coding information from a single, previously-encoded, unwatermarked video in order to speed up the encodings of the watermarked videos. As a result, the complexity of a fast encoder is only a fraction of the complexity of a full encoder. Due to a high correlation of the re-used coding information with the optimal coding information, the compression efficiency and watermark robustness decrease only slightly. Most importantly, the proposed fast encoder speeds up the compression process with a factor of 115, resulting in a low complexity similar to that of a video decoder. Consequently, video distributors can use the proposed architecture to deliver high-quality watermarked videos on a large-scale without requiring an excessive amount of computational resources.

Video-streaming events such as virtual classrooms and video conferences require a low delay between sender and receiver. In order to achieve this requirement, and to make full use of the bandwidth capacity of each receiver, each client can be provided with a personalized bitstream of which the bit rate is continuously adapted to his current network bandwidth capacity. However, such an approach requires an excessive amount of computationally complex video encoders. Therefore, this paper proposes an architecture based on coding information calculation (CIC) modules and residual encoder (RE) modules. The CIC modules calculate coding information for the video at certain bit rates whereas the RE modules use this information to skip all encoding steps of a traditional encoder, except for the encoding of the residual. By reducing the amount of bits used to encode the residual, the RE modules can then provide bitstreams with personalized bit rates for several users at the same time. Each CIC module has approximately the same computational complexity as a traditional encoder, whereas an RE module has the approximate complexity of a decoder. The proposed architecture was evaluated for the high efficiency video coding standard, showing that the system achieves its goal of drastically reducing the computational complexity of low-delay live-streaming with many participants and suggesting that using less than six CIC modules results in the best tradeoff between compression efficiency and computational complexity.

High-dynamic-range (HDR) technology allows capturing of video content at a wider range of luminance than low-dynamic-range (LDR) video. The resulting video more closely resembles the scene as perceived by the human eye. However, displays currently support only a limited range of HDR. Therefore, both an HDR version and LDR version of a video should be encoded during content acquisition. This means that the cost of encoder hardware in cameras would double. As a solution, this paper proposes a multistream video encoder that allows generating an HDR and LDR version of the same HDR video footage at roughly the same computational complexity as a single encoder, effectively allowing encoding of two dynamic-range versions of the video with a negligible increase in cost. For the LDR version, this multistream encoder results in a bit rate overhead of only 11.6% for the same quality as a two-encoder solution.

High efficiency video coding (HEVC) shows a significant advance in compression efficiency and is considered to be the successor of H.264/AVC. To incorporate the HEVC standard into real-life network applications and a diversity of other applications, efficient bit rate adaptation (transrating) algorithms are required. A current problem of transrating for HEVC is the high computational complexity associated with the encoder part of such a cascaded pixel domain transcoder. This paper focuses on deriving an optimal strategy for reducing the transcoding complexity with a complexity-scalable scheme. We propose different transcoding techniques which are able to reduce the transcoding complexity in both CU and PU optimization levels. At the CU level, CUs can be evaluated in top-to-bottom or bottom-to-top flows, in which the coding information of the input video stream is utilized to reduce the number of evaluations or to early terminate certain evaluations. At the PU level, the PU candidates are adaptively selected based on the probability of PU sizes and the co-located input PU partitioning. Moreover, with the use of different proposed methods, a complexity-scalable transrating scheme can be achieved. Furthermore, the transcoding complexity can be effectively controlled by the machine learning based approach. Simulations show that the proposed techniques provide a superior transcoding performance compared to the state-of-the-art related works. Additionally, the proposed methods can achieve a range of trade-offs between transrating complexity and coding performance. From the proposed schemes, the fastest approach is able to reduce the complexity by 82% while keeping the bitrate loss below 3%.

Communication using internet and digital media is more and more popular. Therefore, the security and privacy of data transmission are highly demanded. One effective technique providing this requirement is information hiding. This technique allows to conceal secret information into a video file, an audio, or a picture. In this paper, we propose a low complexity out-of-the-loop information hiding algorithm for a video pre-encoded with the high efficiency video coding standard. Only selected components such as the motion vector difference and transform coefficients of the video are extracted and modified, bypassing the need of fully decoding and re-encoding the video. In order to reduce the propagation error caused by hiding information, the dependency between video frames is taken into account when distributing the information over the frame. Several embedding strategies are investigated. The experimental results show that the information should be hidden in smaller blocks to reduce quality loss. Using a smart distribution of information across the frames can keep the quality loss under 1 dB PSNR for an information payload of 15 kbps. When such a strategy is used, embedding information in the transform coefficients only slightly outperforms the modification of motion vector differences.

Content providers create different versions of a video to accommodate different end-user devices and network conditions. However, each of these versions requires a resource intensive encoding process. To reduce the computational complexity of the encodings, this paper proposes a fast simultaneous encoder. This encoder takes a single video as input and creates a number of bit streams encoded with different parameters. Only one version of the video is created with a full encode, whereas encoding of the other versions is accelerated by exploiting the correlation with the fully encoded version using machine learning techniques. In a practical scenario, the fast simultaneous encoder achieves a complexity reduction of 67.3% with a bit rate increase of 5.2% compared to performing a full encode of each version.

The rapid emergence and rise of a wide range of electronic devices has led to the need for providing very different levels of video transmission services. The capabilities and performance of these devices determine the type of video streams that they are able to decode. As a way to fulfil their requirements, this paper presents a heterogeneous simultaneous encoding framework of H.264/Advanced Video Coding (AVC) and High Efficiency Video Coding (HEVC) that shares information between encoders in order to reduce the overall encoding time. In this regard, the proposed approach utilizes the H.264/AVC motion vectors of the 16x16 pixels macroblocks as predictors for the HEVC prediction units. As a consequence, the size of the motion estimation search area can be significantly reduced. Results show that an encoding time reduction of 8.95% can be achieved with negligible losses in terms of rate-distortion.

The visualization of (ultra) high-resolution compositions created from multiple input bitstreams requires several decoders at the receiving device. Therefore, not all devices can properly display such compositions. To address this problem, the input streams are decoded, merged into a single video, and re-encoded by a transcoder in the network. However, this approach requires a computationally complex re-encoding step. To reduce this complexity, information from the input bit-streams can be reused during transcoding. In HEVC, simply reusing the original encoding information is not compression efficient if the inserted content is not aligned with the grid of coded blocks. In this paper, we applied feature selection based on a decision tree, which was used in a fast HEVC transcoding model for misaligned content. The performance varies depending on the shift and average transform size in the original sequence, resulting in complexity reductions of up to 76%.